Overcoming gaps: regional collaborative to optimize capacity management and predict length of stay of patients admitted with COVID-19

Optimizing health system volume flexibility: key determinants and strategies during the COVID-19 crisis

PURPOSE

The need to match highly variable demand for healthcare services with existing capacity has been a significant health system challenge experienced by the COVID-19 pandemic. Despite mounting research on various COVID-19 responses in the recent literature, there has not been a structured review examining key determinants of health system volume flexibility. The focus of the present research is to review recent literature based on a conceptual framework developed based on the volume flexibility literature on COVID-19. The review also provides a meaningful way to guide practice and future research in the area.

DESIGN/METHODOLOGY/APPROACH

A literature review was performed guided by the theoretical frameworks developed in the previous volume of flexibility literature. Selected research papers were reviewed and analyzed to identify key determinants of health system volume flexibility.

FINDINGS

Seven determinants of health system volume flexibility under three domains (demand management, capacity management, and performance) included demand management strategies, integration, capacity management strategies, workforce management, technology, quality of disease outcomes, and health system knowledge.

ORIGINALITY/VALUE

The research provides both health systems researchers as well as practitioners with a foundational conceptual framework and knowledge related to health system volume flexibility. In addition, this research identifies future research areas to expand knowledge relevant to health system volume flexibility.

Predictors of discharge disposition and mortality following hospitalization with SARS-CoV-2 infection

IMPORTANCE

The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition.

OBJECTIVE

Characterization of disposition associated factors following SARS-CoV-2.

DESIGN

Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization.

SETTING

Midwest academic health-system.

PARTICIPANTS

Patients above the age 18 years admitted with PCR + SARS-CoV-2.

INTERVENTION

None.

MAIN OUTCOMES

Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH).

RESULTS

We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6-85.1) compared to those discharged home (57.0 years, IQR: 38.2-69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041-1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168-1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060-1.077) and higher EI (RRR:1.09, 95% CI:1.071-1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47-0.88), (RRR 0.48 CI 0.34-0.67) and (RRR 0.586 CI 0.352-0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444-780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99-0.99) and albumin levels 0.342 (CI 0.26-0.45), and higher with increased CRP (RRR 1.006 CI 1.004-1.007) and D-Dimer (RRR 1.070 CI 1.039-1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497-0.798. An increase in D-Dimer (RRR1.033 CI 1.002-1.064) and CRP (RRR1.002 CI1.001-1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC.

CONCLUSION

Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH.

Prediction Model of hospitalization time of COVID-19 patients based on Gradient Boosted Regression Trees

When an outbreak of COVID-19 occurs, it will cause a shortage of medical resources and the surge of demand for hospital beds. Predicting the length of stay (LOS) of COVID-19 patients is helpful to the overall coordination of hospital management and improves the utilization rate of medical resources. The purpose of this paper is to predict LOS for patients with COVID-19, so as to provide hospital management with auxiliary decision-making of medical resource scheduling. We collected the data of 166 COVID-19 patients in a hospital in Xinjiang from July 19, 2020, to August 26, 2020, and carried out a retrospective study. The results showed that the median LOS was 17.0 days, and the average of LOS was 18.06 days. Demographic data and clinical indicators were included as predictive variables to construct a model for predicting the LOS using gradient boosted regression trees (GBRT). The MSE, MAE and MAPE of the model are 23.84, 4.12 and 0.76 respectively. The importance of all the variables involved in the prediction of the model was analyzed, and the clinical indexes creatine kinase-MB (CK-MB), C-reactive protein (CRP), creatine kinase (CK), white blood cell count (WBC) and the age of patients had a higher contribution to the LOS. We found our GBRT model can accurately predict the LOS of COVID-19 patients, which will provide good assistant decision-making for medical management.